README.md

metadata

base_model:
  - GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
  - aisingapore/gemma2-9b-cpt-sea-lionv3-instruct
tags:
  - merge
  - mergekit
license: gemma
language:
  - en
  - id
  - jv
  - su

SahabatAI-Lion-9B-TIES-v1

Based on some research, when a finetuned model is merged with its base model with TIES method, there is possibility the merged model will achieve better output.

gmonsoon/SahabatAI-Lion-9B-TIES-v1 is a merge of the following models:

• GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
• aisingapore/gemma2-9b-cpt-sea-lionv3-instruct

DEMO Spaces: HERE

🧩 Configuration

models:
  - model: GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
    parameters:
      weight: 1
      density: 1
  - model: GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct
    parameters:
      weight: 1
      density: 1
merge_method: ties
base_model: aisingapore/gemma2-9b-cpt-sea-lionv3-instruct
parameters:
  density: 1
  normalize: true
  int8_mask: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "gmonsoon/SahabatAI-Lion-9B-TIES-v1"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])